# -*- coding: utf-8 -*-

from datetime import timedelta
from utils.operators.cluster_for_spark_sql_hook_test_for_airflow_bug_operator import SparkSqlOperator
from jms_route_test.dm.dm_prescription_reach_details_dt import jms_dm__dm_prescription_reach_details_dt
# from jms.time_sensor.time_after_04_45 import time_after_04_45

jms_dm__dm_city_pre_reach_rate_dt = SparkSqlOperator(
    task_id='jms_dm__dm_city_pre_reach_rate_dt',
    task_concurrency=1,
    pool_slots=2,
    master='yarn',
    email=['jokic.wang@jtexpress.com', 'yl_bigdata@yl-scm.com'],
    name='jms_dm__dm_city_pre_reach_rate_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms_route_test/dm/dm_city_pre_reach_rate_dt/execute.hql',
    # sla=timedelta(hours=7),
    driver_memory='10G',
    executor_memory='12G',
    executor_cores=5,
    num_executors=40,
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
          'spark.dynamicAllocation.maxExecutors': 60,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 60,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead': '6G',  # 堆外内存
          'spark.sql.shuffle.partitions': 600
          },
    hiveconf={'hive.exec.dynamic.partition': 'true',  # 动态分区
              'hive.exec.dynamic.partition.mode': 'nonstrict',
              'hive.exec.max.dynamic.partitions': 50,  # 每天生成 50 个分区
              'hive.exec.max.dynamic.partitions.pernode': 50,  # 每天生成 50 个分区
              },
    yarn_queue='pro',
    execution_timeout=timedelta(minutes=60),
)

jms_dm__dm_city_pre_reach_rate_dt << [
    jms_dm__dm_prescription_reach_details_dt
    # time_after_04_45
]
